class_docstr: Nearest centroid classifier.
  
  Each class is represented by its centroid, with test samples classified to
  the class with the nearest centroid.
  
  Read more in the :ref:`User Guide <nearest_centroid_classifier>`.
  
  Parameters
  ----------
  metric : str or callable, default="euclidean"
      Metric to use for distance computation. See the documentation of
      `scipy.spatial.distance
      <https://docs.scipy.org/doc/scipy/reference/spatial.distance.html>`_ and
      the metrics listed in
      :class:`~sklearn.metrics.pairwise.distance_metrics` for valid metric
      values. Note that "wminkowski", "seuclidean" and "mahalanobis" are not
      supported.
  
      The centroids for the samples corresponding to each class is
      the point from which the sum of the distances (according to the metric)
      of all samples that belong to that particular class are minimized.
      If the `"manhattan"` metric is provided, this centroid is the median
      and for all other metrics, the centroid is now set to be the mean.
  
      .. deprecated:: 1.3
          Support for metrics other than `euclidean` and `manhattan` and for
          callables was deprecated in version 1.3 and will be removed in
          version 1.5.
  
      .. versionchanged:: 0.19
          `metric='precomputed'` was deprecated and now raises an error
  
  shrink_threshold : float, default=None
      Threshold for shrinking centroids to remove features.
  
  Attributes
  ----------
  centroids_ : array-like of shape (n_classes, n_features)
      Centroid of each class.
  
  classes_ : array of shape (n_classes,)
      The unique classes labels.
  
  n_features_in_ : int
      Number of features seen during :term:`fit`.
  
      .. versionadded:: 0.24
  
  feature_names_in_ : ndarray of shape (`n_features_in_`,)
      Names of features seen during :term:`fit`. Defined only when `X`
      has feature names that are all strings.
  
      .. versionadded:: 1.0
  
  See Also
  --------
  KNeighborsClassifier : Nearest neighbors classifier.
  
  Notes
  -----
  When used for text classification with tf-idf vectors, this classifier is
  also known as the Rocchio classifier.
  
  References
  ----------
  Tibshirani, R., Hastie, T., Narasimhan, B., & Chu, G. (2002). Diagnosis of
  multiple cancer types by shrunken centroids of gene expression. Proceedings
  of the National Academy of Sciences of the United States of America,
  99(10), 6567-6572. The National Academy of Sciences.
  
  Examples
  --------
  >>> from sklearn.neighbors import NearestCentroid
  >>> import numpy as np
  >>> X = np.array([[-1, -1], [-2, -1], [-3, -2], [1, 1], [2, 1], [3, 2]])
  >>> y = np.array([1, 1, 1, 2, 2, 2])
  >>> clf = NearestCentroid()
  >>> clf.fit(X, y)
  NearestCentroid()
  >>> print(clf.predict([[-0.8, -1]]))
  [1]
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docstr: Fit the NearestCentroid model according to the given training data.
  
  Parameters
  ----------
  X : {array-like, sparse matrix} of shape (n_samples, n_features)
      Training vector, where `n_samples` is the number of samples and
      `n_features` is the number of features.
      Note that centroid shrinking cannot be used with sparse matrices.
  y : array-like of shape (n_samples,)
      Target values.
  
  Returns
  -------
  self : object
      Fitted estimator.
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code: def fit(self, X, y):
      """
      Fit the NearestCentroid model according to the given training data.
  
      Parameters
      ----------
      X : {array-like, sparse matrix} of shape (n_samples, n_features)
          Training vector, where `n_samples` is the number of samples and
          `n_features` is the number of features.
          Note that centroid shrinking cannot be used with sparse matrices.
      y : array-like of shape (n_samples,)
          Target values.
  
      Returns
      -------
      self : object
          Fitted estimator.
      """
      if isinstance(self.metric, str) and self.metric not in (
          "manhattan",
          "euclidean",
      ):
          warnings.warn(
              (
                  "Support for distance metrics other than euclidean and "
                  "manhattan and for callables was deprecated in version "
                  "1.3 and will be removed in version 1.5."
              ),
              FutureWarning,
          )
  
      # If X is sparse and the metric is "manhattan", store it in a csc
      # format is easier to calculate the median.
      if self.metric == "manhattan":
          X, y = self._validate_data(X, y, accept_sparse=["csc"])
      else:
          X, y = self._validate_data(X, y, accept_sparse=["csr", "csc"])
      is_X_sparse = sp.issparse(X)
      if is_X_sparse and self.shrink_threshold:
          raise ValueError(
              "threshold shrinking not supported for sparse input"
          )
      check_classification_targets(y)
  
      n_samples, n_features = X.shape
      le = LabelEncoder()
      y_ind = le.fit_transform(y)
      self.classes_ = classes = le.classes_
      n_classes = classes.size
      if n_classes < 2:
          raise ValueError(
              "The number of classes has to be greater than one; got %d class"
              % (n_classes)
          )
  
      # Mask mapping each class to its members.
      self.centroids_ = np.empty(
          (n_classes, n_features), dtype=np.float64
      )
      # Number of clusters in each class.
      nk = np.zeros(n_classes)
  
      for cur_class in range(n_classes):
          center_mask = y_ind == cur_class
          nk[cur_class] = np.sum(center_mask)
          if is_X_sparse:
              center_mask = np.where(center_mask)[0]
  
          if self.metric == "manhattan":
              # NumPy does not calculate median of sparse matrices.
              if not is_X_sparse:
                  self.centroids_[cur_class] = np.median(
                      X[center_mask], axis=0
                  )
              else:
                  self.centroids_[cur_class] = csc_median_axis_0(
                      X[center_mask]
                  )
          else:
              # TODO(1.5) remove warning when metric is only manhattan or euclidean
              if self.metric != "euclidean":
                  warnings.warn(
                      "Averaging for metrics other than "
                      "euclidean and manhattan not supported. "
                      "The average is set to be the mean."
                  )
              self.centroids_[cur_class] = X[center_mask].mean(axis=0)
  
      if self.shrink_threshold:
          if np.all(np.ptp(X, axis=0) == 0):
              raise ValueError(
                  "All features have zero variance. Division by zero."
              )
          dataset_centroid_ = np.mean(X, axis=0)
  
          # m parameter for determining deviation
          m = np.sqrt((1.0 / nk) - (1.0 / n_samples))
          # Calculate deviation using the standard deviation of centroids.
          variance = (X - self.centroids_[y_ind]) ** 2
          variance = variance.sum(axis=0)
          s = np.sqrt(variance / (n_samples - n_classes))
          s += np.median(
              s
          )  # To deter outliers from affecting the results.
          mm = m.reshape(len(m), 1)  # Reshape to allow broadcasting.
          ms = mm * s
          deviation = (self.centroids_ - dataset_centroid_) / ms
          # Soft thresholding: if the deviation crosses 0 during shrinking,
          # it becomes zero.
          signs = np.sign(deviation)
          deviation = np.abs(deviation) - self.shrink_threshold
          np.clip(deviation, 0, None, out=deviation)
          deviation *= signs
          # Now adjust the centroids using the deviation
          msd = ms * deviation
          self.centroids_ = dataset_centroid_[np.newaxis, :] + msd
      return self
  
file_path: catboost/contrib/python/scikit-learn/py3/sklearn/neighbors/_nearest_centroid.py
imports: import warnings
  from numbers import Real
  import numpy
  from scipy import sparse
  from sklearn.metrics.pairwise import _VALID_METRICS
  from ..base import BaseEstimator, ClassifierMixin, _fit_context
  from ..metrics.pairwise import pairwise_distances_argmin
  from ..preprocessing import LabelEncoder
  from ..utils._param_validation import Interval, StrOptions
  from ..utils.multiclass import check_classification_targets
  from ..utils.sparsefuncs import csc_median_axis_0
  from ..utils.validation import check_is_fitted
label_desc: Use image node_id 2 for calling the NearestCentroid obj's underlying member method code with example usage: obj.fit(X, y) and returns: self